Organizational cognitive neuroscience

Organizational cognitive neuroscience (OCN) is the cognitive neuroscientific study of organizational behavior. OCN lets us start to understand the relationship between our organizational behavior and our brains and allows us to dissect specific social processes at the neurobiological level and apply a wider range of analysis to specific organizational research questions. The current paper examines the utility of OCN to address specific organizational research questions. A brief history and definition of the approach is first provided. Next, a discussion of the rationale for OCN as a research framework is provided, and then, finally, an overview of the range of techniques that the organizational researcher should (or shouldnot) use is described.

The brain in business:the case for organizational cognitive neuroscience?

The application of cognitive neuroscientific techniques to understanding social behaviour has resulted in many discoveries. Yet advocates of the ‘social cognitive neuroscience’ approach maintain that it suffers from a number of limitations. The most notable of these is its distance from any form of real-world applicabity. One solution to this limitation is ‘Organisational Cognitive Neuroscience’ – the study of the cognitive neuroscience of human behaviour in, and in response to, organizations. Given that all of us will spend most of our lives in some sort of work related organisation, organisational cognitive neuroscience allows us to examine the cognitive underpinnings of social behaviour that occurs in what may be our most natural ecology. Here we provide a brief overview of this approach, a definition and also some possible questions that the new approach would be best suited to address.

The social cognitive neuroscience of organisations

Social cognitive neuroscience is an emerging branch of cognitive neuroscience that bridges together social psychology and neuroscience. At its core is an understanding of the relationship between the brain and social interaction. The social cognitive neuroscientist places empirical endeavor within a three–stage framework, and questions falling under the SCN rubric undergo interrogation at each of these three levels. Firstly, we seek to understand a neuroscience of social interactions at the social level. Here we need to understand the motivational and other social factors that drive our behavior and experience in the real world. It goes without saying that any study of the cognitive neuroscience of socially interactive behavior must first be informed by social psychological theory to maintain ecological validity. Second, the social cognitive neuroscientist must be an adroit cognitive psychologist and be able to examine interactive behavior from the cognitive level. It is here that information–processing models and theories are applied to the understanding of our social behavior. Finally, studies at the neural level seek to inform us about the cortical structures, as well as the way they interact with other, in the mediation of the previous cognitive level. This volume brings together contributions from leading thinkers in both the social cognitive neurosciences and business to provide a comprehensive introduction and overview of a social cognitive neuroscience of the business brain. NOTE: Annals volumes are available for sale as individual books or as a journal. For information on institutional journal subscriptions, please visit www.blackwellpublishing.com/nyas. ACADEMY MEMBERS: Please contact the New York Academy of Sciences directly to place your order (www.nyas.org). Members of the New York Academy of Science receive full–text access to the Annals online and discounts on print volumes. Please visit http://www.nyas.org/MemberCenter/Join.aspx for more information about becoming a member

Research possibilities for organisational cognitive neuroscience

In this article, we identify research possibilities for organizational cognitive neuroscience that emerge from the papers in this special issue. We emphasize the intriguing finding that the papers share a common theme-the use of cognitive neuroscience to investigate the role of emotions in organizational behavior; this suggests a research agenda in its own right. We conclude the article by stressing that there is much yet to discover about how the mind works, especially in organizational settings.

Neuroimaging and psychophysiological measurement in organizational research:an agenda for research in organizational cognitive neuroscience

Although organizational research has made tremendous strides in the last century, recent advances in neuroscience and the imaging of functional brain activity remain underused. In fact, even the use of well-established psychophysiological measurement tools is comparatively rare. Following the lead of social cognitive neuroscience, in this review, we conceptualize organizational cognitive neuroscience as a field dedicated to exploring the processes within the brain that underlie or influence human decisions, behaviors, and interactions either (a) within organizations or (b) in response to organizational manifestations or institutions. We discuss organizational cognitive neuroscience, bringing together work that may previously have been characterized rather atomistically, and provide a brief overview of individual methods that may be of use. Subsequently, we discuss the possible convergence and integration of the different neuroimaging and psychophysiological measurement modalities. A brief review of prior work in the field shows a significant need for a more coherent and theory-driven approach to organizational cognitive neuroscience. In response, we discuss a recent example of such work, along with three hypothetical case studies that exemplify the link between organizational and psychological theory and neuroscientific methods.

Causality and synchronisation in complex systems with applications to neuroscience

This thesis presents an investigation, of synchronisation and causality, motivated by problems in computational neuroscience. The thesis addresses both theoretical and practical signal processing issues regarding the estimation of interdependence from a set of multivariate data generated by a complex underlying dynamical system. This topic is driven by a series of problems in neuroscience, which represents the principal background motive behind the material in this work. The underlying system is the human brain and the generative process of the data is based on modern electromagnetic neuroimaging methods . In this thesis, the underlying functional of the brain mechanisms are derived from the recent mathematical formalism of dynamical systems in complex networks. This is justified principally on the grounds of the complex hierarchical and multiscale nature of the brain and it offers new methods of analysis to model its emergent phenomena. A fundamental approach to study the neural activity is to investigate the connectivity pattern developed by the brain’s complex network. Three types of connectivity are important to study: 1) anatomical connectivity refering to the physical links forming the topology of the brain network; 2) effective connectivity concerning with the way the neural elements communicate with each other using the brain’s anatomical structure, through phenomena of synchronisation and information transfer; 3) functional connectivity, presenting an epistemic concept which alludes to the interdependence between data measured from the brain network. The main contribution of this thesis is to present, apply and discuss novel algorithms of functional connectivities, which are designed to extract different specific aspects of interaction between the underlying generators of the data. Firstly, a univariate statistic is developed to allow for indirect assessment of synchronisation in the local network from a single time series. This approach is useful in inferring the coupling as in a local cortical area as observed by a single measurement electrode. Secondly, different existing methods of phase synchronisation are considered from the perspective of experimental data analysis and inference of coupling from observed data. These methods are designed to address the estimation of medium to long range connectivity and their differences are particularly relevant in the context of volume conduction, that is known to produce spurious detections of connectivity. Finally, an asymmetric temporal metric is introduced in order to detect the direction of the coupling between different regions of the brain. The method developed in this thesis is based on a machine learning extensions of the well known concept of Granger causality. The thesis discussion is developed alongside examples of synthetic and experimental real data. The synthetic data are simulations of complex dynamical systems with the intention to mimic the behaviour of simple cortical neural assemblies. They are helpful to test the techniques developed in this thesis. The real datasets are provided to illustrate the problem of brain connectivity in the case of important neurological disorders such as Epilepsy and Parkinson’s disease. The methods of functional connectivity in this thesis are applied to intracranial EEG recordings in order to extract features, which characterize underlying spatiotemporal dynamics before during and after an epileptic seizure and predict seizure location and onset prior to conventional electrographic signs. The methodology is also applied to a MEG dataset containing healthy, Parkinson’s and dementia subjects with the scope of distinguishing patterns of pathological from physiological connectivity.

Visual thinking in recent neuroscience

After a consideration of visual thinking in science the role of such thinking in neuroscience is discussed. Three instances are examined - cortical column, retina, impulse - and it is argued that visual thinking is employed, though in different ways, in each. It lies at the core of neurobiological thought.

Brain, mind and medicine:essays in eighteenth Century neuroscience

There have been no books published on neuroscience in the eighteenth century. Yet this was an important time, with science and medicine in transition. On the one hand, there were wildly speculative theories about the nervous system, many based on Newtonian mechanics and fanciful chemistry. But on the other, this was also a time when empirical research with quantification and experimentation was coming of age. This volume examines the eighteenth-century neuroscience milieu and looks at developments in anatomy, physiology, and medicine that highlight this era, which some people have called the Age of Reason and others the Enlightenment. The book covers such things as the aims of the scientific and medical Enlightenment, how neuroscience adopted electricity as the nerve force, how disorders such as aphasia and hysteria were treated, Mesmerism, and how some of the latest ideas made their way into the culture of the day.

Cognitive neuroscience for the 21st Century

conference review: 2000 Autumn School in Cognitive Neuroscience, 26–29 September 2000, University of Oxford, UK.

Cognitive neuroscience and (very) high level vision

THE PURPOSE OF THIS ARTICLE is two-fold, first to provide a general overview of two of the main cognitive neuroscientific techniques available, specifically functional magnetic resonance imaging (fMRI) and transcranial magnetic stimulation (TMS); and secondly to apply these techniques to elaborate a discussion of an aspect of higher level vision, namely implied motion, that is the perception of movement from a static image.

Cognitive neuroscience:contributions from psychophysiology

Preface

The brain in business:neuromarketing and organisational cognitive neuroscience

The application of cognitive neuroscientific techniques to understanding social behaviour has resulted in many discoveries. Yet advocates of the ‘social cognitive neuroscience’ approach maintain that it suffers from a number of limitations. The most notable of these is its distance from any form of real-world applicabity. One solution to this limitation is ‘Organisational Cognitive Neuroscience’— the study of the cognitive neuroscience of human behaviour in, and in response to, organizations, which are arguably our most natural contemporary ecology. Here we provide a brief overview of this approach, a definition and also some examples of questions that the approach would be best suited to address. Furthemore, we consider neuromarketing as a subfield of organizational cognitive neuroscience, arguing that such a relationship clarifies the role of scholarly marketing research in the area, and provides a welcome emphasis on theoretical rigour.

Cognitive neuroscience: the evolution of a discipline

Preface. The evolution of cognitive neuroscience has been spurred by the development of increasingly sophisticated investigative techniques to study human cognition. In Methods in Mind, experts examine the wide variety of tools available to cognitive neuroscientists, paying particular attention to the ways in which different methods can be integrated to strengthen empirical findings and how innovative uses for established techniques can be developed. The book will be a uniquely valuable resource for the researcher seeking to expand his or her repertoire of investigative techniques. Each chapter explores a different approach. These include transcranial magnetic stimulation, cognitive neuropsychiatry, lesion studies in nonhuman primates, computational modeling, psychophysiology, single neurons and primate behavior, grid computing, eye movements, fMRI, electroencephalography, imaging genetics, magnetoencephalography, neuropharmacology, and neuroendocrinology. As mandated, authors focus on convergence and innovation in their fields; chapters highlight such cross-method innovations as the use of the fMRI signal to constrain magnetoencephalography, the use of electroencephalography (EEG) to guide rapid transcranial magnetic stimulation at a specific frequency, and the successful integration of neuroimaging and genetic analysis. Computational approaches depend on increased computing power, and one chapter describes the use of distributed or grid computing to analyze massive datasets in cyberspace. Each chapter author is a leading authority in the technique discussed.